Research Preview — APIs may change. GitHub
Geocomputation
Geocomputation is the processing of geospatial data to answer spatial questions: How far apart are these two locations? Is this point inside that boundary? Do these regions overlap? The answers seem simple, but computing them correctly — on a curved, non-Euclidean surface, with consistent precision and deterministic results — is harder than it appears.The computational challenge
Spatial computation involves real complexity:- Reference systems — The Earth is not flat, and different coordinate systems represent it differently. Computing distance on a sphere vs. a projected plane vs. a geoid gives different results.
- Computational geometry — Operations like polygon containment, intersection detection, and area calculation require robust algorithms that handle edge cases (self-intersecting polygons, antipodal points, degenerate geometries).
- Precision and determinism — Floating-point arithmetic is not associative. The same computation can produce different results depending on operation order, hardware, or library version. For verifiable results, computation must be deterministic.
Scope of geocomputation in Astral
Astral’s geocomputation capabilities span three areas, at different stages of maturity:| Capability | Status | What it does |
|---|---|---|
| Location proof verification | Available (Research Preview) | Evaluates location proof credibility |
| Geospatial operations | Available (Research Preview) | Distance, containment, intersection, area, length |
| Geospatial AI/ML | Planned | Spatial analysis, prediction, pattern detection |
Verifiable geocomputation
Computation is only useful in adversarial contexts if you can trust the result. For most applications, that means running code on a server and trusting the operator. For applications where the spatial answer triggers real-world consequences that may carry an incentive to lie — a smart contract execution, a compliance determination, an autonomous agent decision — that trust model isn’t sufficient. Astral makes geocomputation verifiable through three approaches, at different stages of development: Trusted Execution Environments (v0). Astral’s Compute engine is designed to run inside a TEE, which provides hardware-level isolation. (It’s a self-contained Docker container and isn’t tied to a specific TEE provider; the current deployment target is EigenCompute.) Under attestation, the TEE guarantees that the attested code executed on the attested inputs, and that the signing key never left the enclave. This is the foundation of Astral Location Services, our hosted service. (Continuous attested operation is not yet funded — see the trust model for current status.) Zero-knowledge circuits (research). ZK proofs would allow verification of correct computation without any trusted hardware — a verifier could confirm the result was computed correctly without re-executing the computation or trusting a TEE manufacturer. This is an active research direction, not yet implemented. Smart contract verification (limited). Some spatial operations could theoretically run onchain, but gas costs and computational limitations make this impractical for most geospatial operations today.Next: Astral Location Services
The hosted TEE service that runs verification and computation
See also:
- API Reference — full endpoint documentation